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Symmetry perception by deep networks

This is the code to reproduce the results in our paper titled "Recurrent connections facilitate symmetry perception in deep networks" (Scientific Reports, 2022)

Authors

Data

Our datasets (synthetic and natural) are available at https://dataverse.harvard.edu/dataverse/symmetry.

Object Recognition DNNs and Transformers

Running experiments (ImageNet DNNs)

To run a demo with six object recognition DNNs pretrained on ImageNet, run this Google Colab notebook .

Running experiments (CLIP Transformer)

To get synthetic and natural data for transfer-training and testing the CLIP transformer, run Symmetry dataset.ipynb.

To run the experiment, run Symmetry_Interacting_with_CLIP.ipynb.

Dilated Convolutional Neural Network and LSTM

Setup

Network training is implemented in Tensorflow 1.14. To guarante that things will successfully run, use the docker image from https://hub.docker.com/r/xboixbosch/tf1.14.

All networks are associated with Experiment objects. Each Experiment object has an unique identifier and defines the network, hyperparameters, and training dataset for that experiment. The Experiment objects for each network are defined in experiments/{network name}.py.

The entry point for generating data and training/testing networks from scratch is main.py, run as detailed below.

Data

All synthetic datasets can be created with script-name = generate_dataset

python main.py \
--experiment_index=${dataset_id} \
--code_path={your-code-path} \
--output_path={your-output-path} \
--run=generate_dataset \
--network={network-name}

Note that the network is specified as the network that the dataset will be used for (one of LSTM3, dilation, or multi_lstm_init).

To build the natural data datasets from the raw pickle files in DataVerse, specify the additional argument --raw_natural_data_path with the path to the pickle files.

Running experiments for Dilated and LSTM

The Dilated and LSTM networks can be trained as follows:

python main.py \
--experiment_index={experiment_id} \
--code_path={your-code-path} \
--output_path={your-output-path} \
--run=train \
--network={network-name}

Where experiment_id specifies the Experiment object (defining the hyperparameters, training dataset, etc).

To test networks on test datasets, run:

python main.py \
--experiment_index={experiment_id} \
--code_path={your-code-path} \
--output_path={your-output-path} \
--run=evaluate_generalization \
--network={network-name}